From the point of view of using CVaR as an optimization objective, the (static) optimization of
the CVaR value for a single period can be done efficiently, by utilizing its primal variational
representation [1]}. In the multi-period setting of a Markov decision problem
(MDP), however, the dynamic optimization of the CVaR value of the total cost is challenging. To
begin, this objective is time-inconsistent. Moreover, as observed by [2]} and
[3]}, the optimal action at a point in time may not be completely determined by the
current state of the MDP, but may depend on the entire history, and therefore the conventional
dynamic programming techniques may not work.
